To take AI to the next level, we need a dose of wisdom

There is no doubt that Canada is a global leader in artificial intelligence. Thanks to the foresight of the 2017 Pan-Canadian AI Strategy, the first of its kind globally, investment has transformed research, enterprise, and the attraction of talent. Yet as talk of another “AI winter” abounds, can Canada uphold its momentum? Or have we reached a plateau? To answer this, we should probably understand what such a plateau might look like. And what would climbing higher toward the vision of “machines that do the sorts of things minds can do” mean? We cannot ignore that debate has started to turn. As American scientist Gary Marcus recently articulated, “deep learning is hitting a wall.” Marcus argues that because AI is a tool that essentially recognizes patterns, there is a limit to which mental processes we are going to be able to simulate with the technique. We may end up looking back and noticing that the result of the first five years of Canada’s AI strategy was, essentially, just picking the next layer of low-hanging fruit. Another feature of AI based on deep learning is its insatiable need for more data. To even the casual observer of the progress of modern AI technology, it must now seem implausible to conceive of AI tech without big data. The two have become inextricably linked in our minds: more data is better. Is this a problem? The answer is emphatically “yes” if we want to challenge the emerging doctrines of surveillance and data linkage. Adopting today’s data-driven AI creates a commercial and government imperative for widespread and high-frequency connected surveillance, which brings with it not only issues of privacy, but also of power, agency, and identity, with which we are only now beginning to grapple. Regulation is clearly a crucial part of the picture. Ontario’s 2021 consultation on "trustworthy AI" established some important priorities. Yet a common feature of such efforts is the stickiness of trying to define what "counts" as AI. There is a worry that unless this can be narrowed down, almost any automated decision-making or data-driven decision support system could be included, and then the ability to regulate and legislate becomes unwieldy. Many people tie themselves in knots trying to define “intelligence,” hoping that that will lead us to somewhat of a more complete (and, they often say, more helpful) definition of “artificial intelligence.” Much of this misses the point, at least when deciding when to delegate to a machine an activity previously done in society by human minds. Once we have taken this step, we have already admitted a certain AI-ness to it. On the other hand, others have made the decision to stop using the terms “artificial intelligence” and “machine learning” altogether, preferring specifics about the technology and who is responsible for it. AI has become, Emily Tucker argues, “a phrase that now functions in the vernacular primarily to obfuscate, alienate, and glamorize.” There is no doubt that we ought to engineer machines for accountability, or that those who build and operate them are responsible for the machine’s actions. But we have already reached the point at which neither full control nor understanding can always be assumed. What then? This relinquishing of control is not simply a bug with AI; for many it is a necessary feature of what an autonomous mind-like machine would require. Perhaps in some ways we need to shift towards thinking about our relationship with someone else’s AI like someone else’s dog: cautious, yet holding the owner fully to account if things go wrong. And as Stanford University professors Byron Reeves and Clifford Nass famously found, people routinely and naturally treat machines like people, whether it’s being polite to a voice assistant or feeling sympathy for robots. The question runs deeper: when is AI based on deep learning the kind of AI that we want to delegate to? Or is it missing something else profoundly mind-like? Today’s AI technologies contain an unusual imbalance of insight and understanding: new insights arise from the models, while many of the “qualities” that a human mind would have brought are utterly absent. Important aspects of our mental activity are, as yet, nowhere near delegated. There is an important distinction to make here between AI as a simulated or synthetic mind, and “AI” as speech, the marketing term so often used to obscure, divert, or confuse. So, what ought future Canadian AI strategy strive for? If the answer is more computation, bigger datasets, and better training algorithms, then we will indeed have reached a plateau. The world already knows how to play that game, and “AI” marketing gives us a clue as to where it goes next. If “AI”-as-surveillance is the extent of our modern-day vision of intelligent machines, then we will have failed millions of Canadians, and especially those at the poor end of structural power relationships. On the other hand, the worry of an “AI winter” only exists if we cannot fathom how to see spring. Perhaps the time has come for a radical rethink of what synthetic minds we want operating within our society. Are these the hyper-rational, data-hungry prediction machines of the late 2010s? Or ought we to expect, and challenge the immense talent within Canada, for more? It has long been a vision of many in AI to conceive of machines capable of a richer version of a mind than that imagined by data-driven problem solving alone; to include reflection, empathy, pause-for-thought, creativity, sociality, nuance, trust, and judgement, not just prediction. In short, let’s challenge ourselves to imagine machines with a dose of wisdom. And let’s bring a dose of wisdom to how we approach the use of machines in our society, too. Dr. Peter Lewis holds a Canada Research Chair in Trustworthy Artificial Intelligence at Ontario Tech University. The Hill Times